/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */

/*
 Copyright (C) 2015 Thema Consulting SA

 This file is part of QuantLib, a free-software/open-source library
 for financial quantitative analysts and developers - http://quantlib.org/

 QuantLib is free software: you can redistribute it and/or modify it
 under the terms of the QuantLib license.  You should have received a
 copy of the license along with this program; if not, please email
 <quantlib-dev@lists.sf.net>. The license is also available online at
 <http://quantlib.org/license.shtml>.

 This program is distributed in the hope that it will be useful, but WITHOUT
 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 FOR A PARTICULAR PURPOSE.  See the license for more details.
*/

/*! \file binomialdoublebarrierengine.hpp
    \brief Binomial Double Barrier option engine
*/

#ifndef quantlib_binomial_double_barrier_engine_hpp
#define quantlib_binomial_double_barrier_engine_hpp

#include <ql/methods/lattices/binomialtree.hpp>
#include <ql/methods/lattices/bsmlattice.hpp>
#include <ql/math/distributions/normaldistribution.hpp>
#include <ql/experimental/barrieroption/discretizeddoublebarrieroption.hpp>
#include <ql/processes/blackscholesprocess.hpp>
#include <ql/termstructures/yield/flatforward.hpp>
#include <ql/termstructures/volatility/equityfx/blackconstantvol.hpp>

namespace QuantLib {

    //! Pricing engine for double barrier options using binomial trees
    /*! \ingroup barrierengines

        \note This engine requires a the discretized option classes. 
        By default uses a standard binomial implementation, but it can
        also work with DiscretizedDermanKaniDoubleBarrierOption to
        implement a Derman-Kani optimization.

        \test the correctness of the returned values is tested by
              checking it against analytic results.
    */
    template <class T, class D = DiscretizedDoubleBarrierOption>
    class BinomialDoubleBarrierEngine : public DoubleBarrierOption::engine {
      public:
        BinomialDoubleBarrierEngine(
             const boost::shared_ptr<GeneralizedBlackScholesProcess>& process,
             Size timeSteps)
        : process_(process), timeSteps_(timeSteps) {
            QL_REQUIRE(timeSteps>0,
                       "timeSteps must be positive, " << timeSteps <<
                       " not allowed");
            registerWith(process_);
        }
        void calculate() const;
      private:
        boost::shared_ptr<GeneralizedBlackScholesProcess> process_;
        Size timeSteps_;
    };


    // template definitions

    template <class T, class D>
    void BinomialDoubleBarrierEngine<T,D>::calculate() const {

        DayCounter rfdc  = process_->riskFreeRate()->dayCounter();
        DayCounter divdc = process_->dividendYield()->dayCounter();
        DayCounter voldc = process_->blackVolatility()->dayCounter();
        Calendar volcal = process_->blackVolatility()->calendar();

        Real s0 = process_->stateVariable()->value();
        QL_REQUIRE(s0 > 0.0, "negative or null underlying given");
        Volatility v = process_->blackVolatility()->blackVol(
            arguments_.exercise->lastDate(), s0);
        Date maturityDate = arguments_.exercise->lastDate();
        Rate r = process_->riskFreeRate()->zeroRate(maturityDate,
            rfdc, Continuous, NoFrequency);
        Rate q = process_->dividendYield()->zeroRate(maturityDate,
            divdc, Continuous, NoFrequency);
        Date referenceDate = process_->riskFreeRate()->referenceDate();

        // binomial trees with constant coefficient
        Handle<YieldTermStructure> flatRiskFree(
            boost::shared_ptr<YieldTermStructure>(
                new FlatForward(referenceDate, r, rfdc)));
        Handle<YieldTermStructure> flatDividends(
            boost::shared_ptr<YieldTermStructure>(
                new FlatForward(referenceDate, q, divdc)));
        Handle<BlackVolTermStructure> flatVol(
            boost::shared_ptr<BlackVolTermStructure>(
                new BlackConstantVol(referenceDate, volcal, v, voldc)));

        boost::shared_ptr<StrikedTypePayoff> payoff =
            boost::dynamic_pointer_cast<StrikedTypePayoff>(arguments_.payoff);
        QL_REQUIRE(payoff, "non-striked payoff given");

        Time maturity = rfdc.yearFraction(referenceDate, maturityDate);

        boost::shared_ptr<StochasticProcess1D> bs(
                         new GeneralizedBlackScholesProcess(
                                      process_->stateVariable(),
                                      flatDividends, flatRiskFree, flatVol));

        TimeGrid grid(maturity, timeSteps_);

        boost::shared_ptr<T> tree(new T(bs, maturity, timeSteps_,
                                        payoff->strike()));

        boost::shared_ptr<BlackScholesLattice<T> > lattice(
            new BlackScholesLattice<T>(tree, r, maturity, timeSteps_));
        
        D option(arguments_, *process_, grid);
        option.initialize(lattice, maturity);

        // Partial derivatives calculated from various points in the
        // binomial tree 
        // (see J.C.Hull, "Options, Futures and other derivatives", 6th edition, pp 397/398)

        // Rollback to third-last step, and get underlying prices (s2) &
        // option values (p2) at this point
        option.rollback(grid[2]);
        Array va2(option.values());
        QL_ENSURE(va2.size() == 3, "Expect 3 nodes in grid at second step");
        Real p2u = va2[2]; // up
        Real p2m = va2[1]; // mid
        Real p2d = va2[0]; // down (low)
        Real s2u = lattice->underlying(2, 2); // up price
        Real s2m = lattice->underlying(2, 1); // middle price
        Real s2d = lattice->underlying(2, 0); // down (low) price

        // calculate gamma by taking the first derivate of the two deltas
        Real delta2u = (p2u - p2m)/(s2u-s2m);
        Real delta2d = (p2m-p2d)/(s2m-s2d);
        Real gamma = (delta2u - delta2d) / ((s2u-s2d)/2);

        // Rollback to second-last step, and get option values (p1) at
        // this point
        option.rollback(grid[1]);
        Array va(option.values());
        QL_ENSURE(va.size() == 2, "Expect 2 nodes in grid at first step");
        Real p1u = va[1];
        Real p1d = va[0];
        Real s1u = lattice->underlying(1, 1); // up (high) price
        Real s1d = lattice->underlying(1, 0); // down (low) price

        Real delta = (p1u - p1d) / (s1u - s1d);

        // Finally, rollback to t=0
        option.rollback(0.0);
        Real p0 = option.presentValue();

        results_.value = p0;
        results_.delta = delta;
        results_.gamma = gamma;
        // theta can be approximated by calculating the numerical derivative
        // between mid value at third-last step and at t0. The underlying price
        // is the same, only time varies.
        results_.theta = (p2m - p0) / grid[2];
    }

}


#endif
